---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: malayalam_combined_
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/krishnan-aravind/huggingface/runs/m81qlwga)
# malayalam_combined_

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4712
- Wer: 0.4649

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.8201        | 0.2031 | 500   | 0.8317          | 0.6757 |
| 0.7455        | 0.4063 | 1000  | 0.7271          | 0.6119 |
| 0.6928        | 0.6094 | 1500  | 0.6823          | 0.6083 |
| 0.6497        | 0.8125 | 2000  | 0.6775          | 0.5955 |
| 0.5968        | 1.0156 | 2500  | 0.6554          | 0.5837 |
| 0.594         | 1.2188 | 3000  | 0.6127          | 0.5772 |
| 0.558         | 1.4219 | 3500  | 0.6149          | 0.5574 |
| 0.5906        | 1.6250 | 4000  | 0.5856          | 0.5485 |
| 0.5846        | 1.8282 | 4500  | 0.5894          | 0.5413 |
| 0.5504        | 2.0313 | 5000  | 0.5571          | 0.5310 |
| 0.5059        | 2.2344 | 5500  | 0.5735          | 0.5542 |
| 0.5019        | 2.4375 | 6000  | 0.5555          | 0.5278 |
| 0.5274        | 2.6407 | 6500  | 0.5592          | 0.5111 |
| 0.5113        | 2.8438 | 7000  | 0.5391          | 0.5318 |
| 0.4624        | 3.0469 | 7500  | 0.5191          | 0.5189 |
| 0.4696        | 3.2501 | 8000  | 0.5312          | 0.5072 |
| 0.4866        | 3.4532 | 8500  | 0.5208          | 0.5167 |
| 0.4817        | 3.6563 | 9000  | 0.5141          | 0.4966 |
| 0.5053        | 3.8594 | 9500  | 0.5104          | 0.5010 |
| 0.4541        | 4.0626 | 10000 | 0.5247          | 0.5130 |
| 0.4761        | 4.2657 | 10500 | 0.5287          | 0.5157 |
| 0.4385        | 4.4688 | 11000 | 0.5196          | 0.5018 |
| 0.4698        | 4.6719 | 11500 | 0.5286          | 0.5120 |
| 0.4631        | 4.8751 | 12000 | 0.5045          | 0.4957 |
| 0.4269        | 5.0782 | 12500 | 0.5102          | 0.5018 |
| 0.4253        | 5.2813 | 13000 | 0.5085          | 0.4949 |
| 0.425         | 5.4845 | 13500 | 0.5209          | 0.4894 |
| 0.4391        | 5.6876 | 14000 | 0.5037          | 0.4900 |
| 0.4206        | 5.8907 | 14500 | 0.5265          | 0.4802 |
| 0.4087        | 6.0938 | 15000 | 0.5044          | 0.4829 |
| 0.4112        | 6.2970 | 15500 | 0.4962          | 0.4860 |
| 0.3864        | 6.5001 | 16000 | 0.4823          | 0.4777 |
| 0.4403        | 6.7032 | 16500 | 0.4898          | 0.4808 |
| 0.3942        | 6.9064 | 17000 | 0.4821          | 0.4808 |
| 0.386         | 7.1095 | 17500 | 0.4804          | 0.4789 |
| 0.3752        | 7.3126 | 18000 | 0.4735          | 0.4662 |
| 0.3863        | 7.5157 | 18500 | 0.4680          | 0.4662 |
| 0.3767        | 7.7189 | 19000 | 0.4692          | 0.4610 |
| 0.3942        | 7.9220 | 19500 | 0.4700          | 0.4721 |
| 0.3502        | 8.1251 | 20000 | 0.4759          | 0.4742 |
| 0.3504        | 8.3283 | 20500 | 0.4702          | 0.4653 |
| 0.3594        | 8.5314 | 21000 | 0.4712          | 0.4649 |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 1.14.0a0+44dac51
- Datasets 2.16.1
- Tokenizers 0.19.1